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Sensors 2018, 18(11), 3953; https://doi.org/10.3390/s18113953

A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Department of Informatics Engineering, University of Coimbra, Polo II-Pinhal de Marrocos, 3030-290 Coimbra, Portugal
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Received: 14 September 2018 / Revised: 8 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)
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Abstract

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments. View Full-Text
Keywords: smart environments; Internet of Things; indoor occupancy; machine learning; data analysis smart environments; Internet of Things; indoor occupancy; machine learning; data analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Abade, B.; Perez Abreu, D.; Curado, M. A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments. Sensors 2018, 18, 3953.

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